diff --git a/docs/source/quickstart/文本分类.rst b/docs/source/quickstart/文本分类.rst index d6a20ae2..65ef39c9 100644 --- a/docs/source/quickstart/文本分类.rst +++ b/docs/source/quickstart/文本分类.rst @@ -7,7 +7,7 @@ 1, 商务大床房，房间很大，床有2M宽，整体感觉经济实惠不错! -其中开头的1是只这条评论的标签，表示是正面的情绪。我们将使用到的数据可以通过 `此链接 `_ +其中开头的1是只这条评论的标签，表示是正面的情绪。我们将使用到的数据可以通过 `此链接 `_ 下载并解压，当然也可以通过fastNLP自动下载该数据。 数据中的内容如下图所示。接下来，我们将用fastNLP在这个数据上训练一个分类网络。 @@ -73,11 +73,12 @@ DataBundle的相关介绍，可以参考 :class:`~fastNLP.io.DataBundle` 。我 .. code-block:: text - DataSet({'raw_chars': 选择珠江花园的原因就是方便，有电动扶梯直接到达海边，周围餐馆、食廊、商场、超市、摊位一应俱全。酒店装修一般，但还算整洁。 泳池在大堂的屋顶，因此很小，不过女儿倒是喜欢。 包的早餐是西式的，还算丰富。 服务吗，一般 type=str, - 'target': 1 type=str}, - {'raw_chars': 15.4寸笔记本的键盘确实爽，基本跟台式机差不多了，蛮喜欢数字小键盘，输数字特方便，样子也很美观，做工也相当不错 type=str, - 'target': 1 type=str}) - + +-----------------------------+--------+ + | raw_chars | target | + +-----------------------------+--------+ + | 选择珠江花园的原因就是方... | 1 | + | 15.4寸笔记本的键盘确实爽... | 1 | + +-----------------------------+--------+ (2) 预处理数据 ~~~~~~~~~~~~~~~~~~~~ @@ -121,14 +122,12 @@ fastNLP中也提供了多种数据集的处理类，这里我们直接使用fast .. code-block:: text - DataSet({'raw_chars': 选择珠江花园的原因就是方便，有电动扶梯直接到达海边，周围餐馆、食廊、商场、超市、摊位一应俱全。酒店装修一般，但还算整洁。 泳池在大堂的屋顶，因此很小，不过女儿倒是喜欢。 包的早餐是西式的，还算丰富。 服务吗，一般 type=str, - 'target': 1 type=int, - 'chars': [338, 464, 1400, 784, 468, 739, 3, 289, 151, 21, 5, 88, 143, 2, 9, 81, 134, 2573, 766, 233, 196, 23, 536, 342, 297, 2, 405, 698, 132, 281, 74, 744, 1048, 74, 420, 387, 74, 412, 433, 74, 2021, 180, 8, 219, 1929, 213, 4, 34, 31, 96, 363, 8, 230, 2, 66, 18, 229, 331, 768, 4, 11, 1094, 479, 17, 35, 593, 3, 1126, 967, 2, 151, 245, 12, 44, 2, 6, 52, 260, 263, 635, 5, 152, 162, 4, 11, 336, 3, 154, 132, 5, 236, 443, 3, 2, 18, 229, 761, 700, 4, 11, 48, 59, 653, 2, 8, 230] type=list, - 'seq_len': 106 type=int}, - {'raw_chars': 15.4寸笔记本的键盘确实爽，基本跟台式机差不多了，蛮喜欢数字小键盘，输数字特方便，样子也很美观，做工也相当不错 type=str, - 'target': 1 type=int, - 'chars': [50, 133, 20, 135, 945, 520, 343, 24, 3, 301, 176, 350, 86, 785, 2, 456, 24, 461, 163, 443, 128, 109, 6, 47, 7, 2, 916, 152, 162, 524, 296, 44, 301, 176, 2, 1384, 524, 296, 259, 88, 143, 2, 92, 67, 26, 12, 277, 269, 2, 188, 223, 26, 228, 83, 6, 63] type=list, - 'seq_len': 56 type=int}) + +-----------------+--------+-----------------+---------+ + | raw_chars | target | chars | seq_len | + +-----------------+--------+-----------------+---------+ + | 选择珠江花园... | 0 | [338, 464, 1... | 106 | + | 15.4寸笔记本... | 0 | [50, 133, 20... | 56 | + +-----------------+--------+-----------------+---------+ 新增了一列为数字列表的chars，以及变为数字的target列。可以看出这两列的名称和刚好与data\_bundle中两个Vocabulary的名称是一致的，我们可以打印一下Vocabulary看一下里面的内容。 @@ -183,11 +182,6 @@ fastNLP支持使用名字指定的Embedding以及相关说明可以参见 :mod:` (4) 创建模型 ~~~~~~~~~~~~ -这里我们使用到的模型结构如下所示 - -.. todo:: - 补图 - .. code-block:: python from torch import nn @@ -261,64 +255,24 @@ fastNLP提供了Trainer对象来组织训练过程，包括完成loss计算(所 Evaluate data in 0.01 seconds! training epochs started 2019-09-03-23-57-10 - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=3000), HTML(value='')), layout=Layout(display… - - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… - Evaluate data in 0.43 seconds! Evaluation on dev at Epoch 1/10. Step:300/3000: AccuracyMetric: acc=0.81 - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… - Evaluate data in 0.44 seconds! Evaluation on dev at Epoch 2/10. Step:600/3000: AccuracyMetric: acc=0.8675 - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… - Evaluate data in 0.44 seconds! Evaluation on dev at Epoch 3/10. Step:900/3000: AccuracyMetric: acc=0.878333 - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… - - Evaluate data in 0.43 seconds! - Evaluation on dev at Epoch 4/10. Step:1200/3000: - AccuracyMetric: acc=0.873333 - - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… - - Evaluate data in 0.44 seconds! - Evaluation on dev at Epoch 5/10. Step:1500/3000: - AccuracyMetric: acc=0.878333 - - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… - - Evaluate data in 0.42 seconds! - Evaluation on dev at Epoch 6/10. Step:1800/3000: - AccuracyMetric: acc=0.895833 - - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… - - Evaluate data in 0.44 seconds! - Evaluation on dev at Epoch 7/10. Step:2100/3000: - AccuracyMetric: acc=0.8975 - - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… - - Evaluate data in 0.43 seconds! - Evaluation on dev at Epoch 8/10. Step:2400/3000: - AccuracyMetric: acc=0.894167 - - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… + .... Evaluate data in 0.48 seconds! Evaluation on dev at Epoch 9/10. Step:2700/3000: AccuracyMetric: acc=0.8875 - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=38), HTML(value='')), layout=Layout(display='… - Evaluate data in 0.43 seconds! Evaluation on dev at Epoch 10/10. Step:3000/3000: AccuracyMetric: acc=0.895833 @@ -327,8 +281,6 @@ fastNLP提供了Trainer对象来组织训练过程，包括完成loss计算(所 AccuracyMetric: acc=0.8975 Reloaded the best model. - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=19), HTML(value='')), layout=Layout(display='… - Evaluate data in 0.34 seconds! [tester] AccuracyMetric: acc=0.8975 @@ -375,8 +327,8 @@ fastNLP提供了Trainer对象来组织训练过程，包括完成loss计算(所 .. code-block:: text - loading vocabulary file /home/yh/.fastNLP/embedding/bert-chinese-wwm/vocab.txt - Load pre-trained BERT parameters from file /home/yh/.fastNLP/embedding/bert-chinese-wwm/chinese_wwm_pytorch.bin. + loading vocabulary file ~/.fastNLP/embedding/bert-chinese-wwm/vocab.txt + Load pre-trained BERT parameters from file ~/.fastNLP/embedding/bert-chinese-wwm/chinese_wwm_pytorch.bin. Start to generating word pieces for word. Found(Or segment into word pieces) 4286 words out of 4409. input fields after batch(if batch size is 2): @@ -390,22 +342,14 @@ fastNLP提供了Trainer对象来组织训练过程，包括完成loss计算(所 Evaluate data in 0.05 seconds! training epochs started 2019-09-04-00-02-37 - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=3600), HTML(value='')), layout=Layout(display… - - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=150), HTML(value='')), layout=Layout(display=… - Evaluate data in 15.89 seconds! Evaluation on dev at Epoch 1/3. Step:1200/3600: AccuracyMetric: acc=0.9 - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=150), HTML(value='')), layout=Layout(display=… - Evaluate data in 15.92 seconds! Evaluation on dev at Epoch 2/3. Step:2400/3600: AccuracyMetric: acc=0.904167 - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=150), HTML(value='')), layout=Layout(display=… - Evaluate data in 15.91 seconds! Evaluation on dev at Epoch 3/3. Step:3600/3600: AccuracyMetric: acc=0.918333 @@ -415,8 +359,6 @@ fastNLP提供了Trainer对象来组织训练过程，包括完成loss计算(所 Reloaded the best model. Performance on test is: - HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=19), HTML(value='')), layout=Layout(display='… - Evaluate data in 29.24 seconds! [tester] AccuracyMetric: acc=0.919167